Figure1D

Load Data

dataset <- read.delim("raw_data/Figure1D.txt", stringsAsFactors = FALSE)

dataset$genotype <-  factor(gsub(" $","",dataset$genotype), levels = c("WT", "PARP1 KO", "ALC1 KO #1", "ALC1 KO #2", "ALC1 KO #3", "ALC1 KO PARP1 KO #1", "ALC1 KO PARP1 KO #2"))
                            
dataset$Experiment <- factor(rep(paste0("exp", 1:(length(dataset$genotype)/length(levels(dataset$genotype)))), each=length(unique(dataset$genotype))))

# genotype+Experiment gives unique ID (UID)
dataset$UID <- factor(paste(dataset$Experiment, dataset$genotype))

# wide format
kable(dataset, row.names = F)

genotype	NT	olaparib_30nM	olaparib_300nM	olaparib_3000nM	Experiment	UID
WT	2550	2460	1504	1112	exp1	exp1 WT
PARP1 KO	2816	2652	2640	1648	exp1	exp1 PARP1 KO
ALC1 KO #1	2448	1016	860	560	exp1	exp1 ALC1 KO #1
ALC1 KO #2	4024	2176	1792	1117	exp1	exp1 ALC1 KO #2
ALC1 KO #3	3296	1524	1274	826	exp1	exp1 ALC1 KO #3
ALC1 KO PARP1 KO #1	3376	2877	2496	1664	exp1	exp1 ALC1 KO PARP1 KO #1
ALC1 KO PARP1 KO #2	3332	2912	2164	2080	exp1	exp1 ALC1 KO PARP1 KO #2
WT	3296	2512	1864	1256	exp2	exp2 WT
PARP1 KO	2992	2632	2480	1832	exp2	exp2 PARP1 KO
ALC1 KO #1	2860	1456	1060	636	exp2	exp2 ALC1 KO #1
ALC1 KO #2	3440	1852	928	664	exp2	exp2 ALC1 KO #2
ALC1 KO #3	2976	1776	1266	832	exp2	exp2 ALC1 KO #3
ALC1 KO PARP1 KO #1	4384	4120	3176	2864	exp2	exp2 ALC1 KO PARP1 KO #1
ALC1 KO PARP1 KO #2	2704	2328	2064	1416	exp2	exp2 ALC1 KO PARP1 KO #2
WT	2784	2704	2184	1424	exp3	exp3 WT
PARP1 KO	1759	1714	1688	1219	exp3	exp3 PARP1 KO
ALC1 KO #1	3800	2452	1988	1078	exp3	exp3 ALC1 KO #1
ALC1 KO #2	3688	2628	2024	1352	exp3	exp3 ALC1 KO #2
ALC1 KO #3	3504	2592	2184	1344	exp3	exp3 ALC1 KO #3
ALC1 KO PARP1 KO #1	3984	3532	3104	3072	exp3	exp3 ALC1 KO PARP1 KO #1
ALC1 KO PARP1 KO #2	1056	967	876	620	exp3	exp3 ALC1 KO PARP1 KO #2
WT	3288	2792	2496	1742	exp4	exp4 WT
PARP1 KO	1631	1554	1468	1058	exp4	exp4 PARP1 KO
ALC1 KO #1	3264	1932	1488	874	exp4	exp4 ALC1 KO #1
ALC1 KO #2	3488	1840	1768	1040	exp4	exp4 ALC1 KO #2
ALC1 KO #3	3024	2016	1428	822	exp4	exp4 ALC1 KO #3
ALC1 KO PARP1 KO #1	3600	2880	2864	2328	exp4	exp4 ALC1 KO PARP1 KO #1
ALC1 KO PARP1 KO #2	1776	1746	1507	906	exp4	exp4 ALC1 KO PARP1 KO #2

library(reshape2)
# reshape to long format
dataset <- melt(dataset, variable.name = "Treatment", value.name = "Counts")

dataset$genotype <- relevel(dataset$genotype, ref = "WT")
dataset$UID <- relevel(dataset$UID, ref = "exp1 WT")

dataset$Olaparib <- gsub("NT","1",dataset$Treatment)
dataset$Olaparib <- gsub("olaparib_|nM","",dataset$Olaparib)
dataset$Olaparib <- log10(as.integer(dataset$Olaparib))




dataset$Offset <- NA
for(uid in levels(dataset$UID)){
        dataset$Offset[dataset$UID == uid] <- mean(dataset$Counts[dataset$UID == uid])
}

dataset$NormCounts <- dataset$Counts / dataset$Offset



dataset$Offset2 <- NA
for(gidx in levels(dataset$genotype)){
        dataset$Offset2[dataset$genotype == gidx] <- mean(dataset$NormCounts[dataset$genotype == gidx & dataset$Olaparib == 0])
}

dataset$NormCounts2 <- dataset$NormCounts / dataset$Offset2



# long format
kable(dataset, row.names = F)

genotype	Experiment	UID	Treatment	Counts	Olaparib	Offset	NormCounts	Offset2	NormCounts2
WT	exp1	exp1 WT	NT	2550	0.000000	1906.50	1.3375295	1.328293	1.0069537
PARP1 KO	exp1	exp1 PARP1 KO	NT	2816	0.000000	2439.00	1.1545715	1.151065	1.0030467
ALC1 KO #1	exp1	exp1 ALC1 KO #1	NT	2448	0.000000	1221.00	2.0049140	1.816617	1.1036527
ALC1 KO #2	exp1	exp1 ALC1 KO #2	NT	4024	0.000000	2277.25	1.7670436	1.750702	1.0093341
ALC1 KO #3	exp1	exp1 ALC1 KO #3	NT	3296	0.000000	1730.00	1.9052023	1.689658	1.1275670
ALC1 KO PARP1 KO #1	exp1	exp1 ALC1 KO PARP1 KO #1	NT	3376	0.000000	2603.25	1.2968405	1.225043	1.0586077
ALC1 KO PARP1 KO #2	exp1	exp1 ALC1 KO PARP1 KO #2	NT	3332	0.000000	2622.00	1.2707857	1.234693	1.0292324
WT	exp2	exp2 WT	NT	3296	0.000000	2232.00	1.4767025	1.328293	1.1117295
PARP1 KO	exp2	exp2 PARP1 KO	NT	2992	0.000000	2484.00	1.2045089	1.151065	1.0464302
ALC1 KO #1	exp2	exp2 ALC1 KO #1	NT	2860	0.000000	1503.00	1.9028609	1.816617	1.0474751
ALC1 KO #2	exp2	exp2 ALC1 KO #2	NT	3440	0.000000	1721.00	1.9988379	1.750702	1.1417349
ALC1 KO #3	exp2	exp2 ALC1 KO #3	NT	2976	0.000000	1712.50	1.7378102	1.689658	1.0284984
ALC1 KO PARP1 KO #1	exp2	exp2 ALC1 KO PARP1 KO #1	NT	4384	0.000000	3636.00	1.2057206	1.225043	0.9842267
ALC1 KO PARP1 KO #2	exp2	exp2 ALC1 KO PARP1 KO #2	NT	2704	0.000000	2128.00	1.2706767	1.234693	1.0291441
WT	exp3	exp3 WT	NT	2784	0.000000	2274.00	1.2242744	1.328293	0.9216900
PARP1 KO	exp3	exp3 PARP1 KO	NT	1759	0.000000	1595.00	1.1028213	1.151065	0.9580881
ALC1 KO #1	exp3	exp3 ALC1 KO #1	NT	3800	0.000000	2329.50	1.6312513	1.816617	0.8979611
ALC1 KO #2	exp3	exp3 ALC1 KO #2	NT	3688	0.000000	2423.00	1.5220801	1.750702	0.8694111
ALC1 KO #3	exp3	exp3 ALC1 KO #3	NT	3504	0.000000	2406.00	1.4563591	1.689658	0.8619255
ALC1 KO PARP1 KO #1	exp3	exp3 ALC1 KO PARP1 KO #1	NT	3984	0.000000	3423.00	1.1638913	1.225043	0.9500816
ALC1 KO PARP1 KO #2	exp3	exp3 ALC1 KO PARP1 KO #2	NT	1056	0.000000	879.75	1.2003410	1.234693	0.9721780
WT	exp4	exp4 WT	NT	3288	0.000000	2579.50	1.2746656	1.328293	0.9596268
PARP1 KO	exp4	exp4 PARP1 KO	NT	1631	0.000000	1427.75	1.1423569	1.151065	0.9924350
ALC1 KO #1	exp4	exp4 ALC1 KO #1	NT	3264	0.000000	1889.50	1.7274411	1.816617	0.9509111
ALC1 KO #2	exp4	exp4 ALC1 KO #2	NT	3488	0.000000	2034.00	1.7148476	1.750702	0.9795198
ALC1 KO #3	exp4	exp4 ALC1 KO #3	NT	3024	0.000000	1822.50	1.6592593	1.689658	0.9820091
ALC1 KO PARP1 KO #1	exp4	exp4 ALC1 KO PARP1 KO #1	NT	3600	0.000000	2918.00	1.2337217	1.225043	1.0070840
ALC1 KO PARP1 KO #2	exp4	exp4 ALC1 KO PARP1 KO #2	NT	1776	0.000000	1483.75	1.1969671	1.234693	0.9694454
WT	exp1	exp1 WT	olaparib_30nM	2460	1.477121	1906.50	1.2903226	1.328293	0.9714141
PARP1 KO	exp1	exp1 PARP1 KO	olaparib_30nM	2652	1.477121	2439.00	1.0873309	1.151065	0.9446306
ALC1 KO #1	exp1	exp1 ALC1 KO #1	olaparib_30nM	1016	1.477121	1221.00	0.8321048	1.816617	0.4580519
ALC1 KO #2	exp1	exp1 ALC1 KO #2	olaparib_30nM	2176	1.477121	2277.25	0.9555385	1.750702	0.5458030
ALC1 KO #3	exp1	exp1 ALC1 KO #3	olaparib_30nM	1524	1.477121	1730.00	0.8809249	1.689658	0.5213629
ALC1 KO PARP1 KO #1	exp1	exp1 ALC1 KO PARP1 KO #1	olaparib_30nM	2877	1.477121	2603.25	1.1051570	1.225043	0.9021369
ALC1 KO PARP1 KO #2	exp1	exp1 ALC1 KO PARP1 KO #2	olaparib_30nM	2912	1.477121	2622.00	1.1106026	1.234693	0.8994972
WT	exp2	exp2 WT	olaparib_30nM	2512	1.477121	2232.00	1.1254480	1.328293	0.8472890
PARP1 KO	exp2	exp2 PARP1 KO	olaparib_30nM	2632	1.477121	2484.00	1.0595813	1.151065	0.9205229
ALC1 KO #1	exp2	exp2 ALC1 KO #1	olaparib_30nM	1456	1.477121	1503.00	0.9687292	1.816617	0.5332601
ALC1 KO #2	exp2	exp2 ALC1 KO #2	olaparib_30nM	1852	1.477121	1721.00	1.0761185	1.750702	0.6146782
ALC1 KO #3	exp2	exp2 ALC1 KO #3	olaparib_30nM	1776	1.477121	1712.50	1.0370803	1.689658	0.6137813
ALC1 KO PARP1 KO #1	exp2	exp2 ALC1 KO PARP1 KO #1	olaparib_30nM	4120	1.477121	3636.00	1.1331133	1.225043	0.9249576
ALC1 KO PARP1 KO #2	exp2	exp2 ALC1 KO PARP1 KO #2	olaparib_30nM	2328	1.477121	2128.00	1.0939850	1.234693	0.8860383
WT	exp3	exp3 WT	olaparib_30nM	2704	1.477121	2274.00	1.1890941	1.328293	0.8952047
PARP1 KO	exp3	exp3 PARP1 KO	olaparib_30nM	1714	1.477121	1595.00	1.0746082	1.151065	0.9335776
ALC1 KO #1	exp3	exp3 ALC1 KO #1	olaparib_30nM	2452	1.477121	2329.50	1.0525864	1.816617	0.5794212
ALC1 KO #2	exp3	exp3 ALC1 KO #2	olaparib_30nM	2628	1.477121	2423.00	1.0846059	1.750702	0.6195262
ALC1 KO #3	exp3	exp3 ALC1 KO #3	olaparib_30nM	2592	1.477121	2406.00	1.0773067	1.689658	0.6375887
ALC1 KO PARP1 KO #1	exp3	exp3 ALC1 KO PARP1 KO #1	olaparib_30nM	3532	1.477121	3423.00	1.0318434	1.225043	0.8422912
ALC1 KO PARP1 KO #2	exp3	exp3 ALC1 KO PARP1 KO #2	olaparib_30nM	967	1.477121	879.75	1.0991759	1.234693	0.8902425
WT	exp4	exp4 WT	olaparib_30nM	2792	1.477121	2579.50	1.0823803	1.328293	0.8148656
PARP1 KO	exp4	exp4 PARP1 KO	olaparib_30nM	1554	1.477121	1427.75	1.0884258	1.151065	0.9455819
ALC1 KO #1	exp4	exp4 ALC1 KO #1	olaparib_30nM	1932	1.477121	1889.50	1.0224927	1.816617	0.5628555
ALC1 KO #2	exp4	exp4 ALC1 KO #2	olaparib_30nM	1840	1.477121	2034.00	0.9046214	1.750702	0.5167192
ALC1 KO #3	exp4	exp4 ALC1 KO #3	olaparib_30nM	2016	1.477121	1822.50	1.1061728	1.689658	0.6546727
ALC1 KO PARP1 KO #1	exp4	exp4 ALC1 KO PARP1 KO #1	olaparib_30nM	2880	1.477121	2918.00	0.9869774	1.225043	0.8056672
ALC1 KO PARP1 KO #2	exp4	exp4 ALC1 KO PARP1 KO #2	olaparib_30nM	1746	1.477121	1483.75	1.1767481	1.234693	0.9530697
WT	exp1	exp1 WT	olaparib_300nM	1504	2.477121	1906.50	0.7888801	1.328293	0.5939052
PARP1 KO	exp1	exp1 PARP1 KO	olaparib_300nM	2640	2.477121	2439.00	1.0824108	1.151065	0.9403562
ALC1 KO #1	exp1	exp1 ALC1 KO #1	olaparib_300nM	860	2.477121	1221.00	0.7043407	1.816617	0.3877211
ALC1 KO #2	exp1	exp1 ALC1 KO #2	olaparib_300nM	1792	2.477121	2277.25	0.7869140	1.750702	0.4494848
ALC1 KO #3	exp1	exp1 ALC1 KO #3	olaparib_300nM	1274	2.477121	1730.00	0.7364162	1.689658	0.4358375
ALC1 KO PARP1 KO #1	exp1	exp1 ALC1 KO PARP1 KO #1	olaparib_300nM	2496	2.477121	2603.25	0.9588015	1.225043	0.7826673
ALC1 KO PARP1 KO #2	exp1	exp1 ALC1 KO PARP1 KO #2	olaparib_300nM	2164	2.477121	2622.00	0.8253242	1.234693	0.6684451
WT	exp2	exp2 WT	olaparib_300nM	1864	2.477121	2232.00	0.8351254	1.328293	0.6287208
PARP1 KO	exp2	exp2 PARP1 KO	olaparib_300nM	2480	2.477121	2484.00	0.9983897	1.151065	0.8673620
ALC1 KO #1	exp2	exp2 ALC1 KO #1	olaparib_300nM	1060	2.477121	1503.00	0.7052562	1.816617	0.3882250
ALC1 KO #2	exp2	exp2 ALC1 KO #2	olaparib_300nM	928	2.477121	1721.00	0.5392214	1.750702	0.3080029
ALC1 KO #3	exp2	exp2 ALC1 KO #3	olaparib_300nM	1266	2.477121	1712.50	0.7392701	1.689658	0.4375265
ALC1 KO PARP1 KO #1	exp2	exp2 ALC1 KO PARP1 KO #1	olaparib_300nM	3176	2.477121	3636.00	0.8734873	1.225043	0.7130256
ALC1 KO PARP1 KO #2	exp2	exp2 ALC1 KO PARP1 KO #2	olaparib_300nM	2064	2.477121	2128.00	0.9699248	1.234693	0.7855597
WT	exp3	exp3 WT	olaparib_300nM	2184	2.477121	2274.00	0.9604222	1.328293	0.7230499
PARP1 KO	exp3	exp3 PARP1 KO	olaparib_300nM	1688	2.477121	1595.00	1.0583072	1.151065	0.9194160
ALC1 KO #1	exp3	exp3 ALC1 KO #1	olaparib_300nM	1988	2.477121	2329.50	0.8534020	1.816617	0.4697755
ALC1 KO #2	exp3	exp3 ALC1 KO #2	olaparib_300nM	2024	2.477121	2423.00	0.8353281	1.750702	0.4771389
ALC1 KO #3	exp3	exp3 ALC1 KO #3	olaparib_300nM	2184	2.477121	2406.00	0.9077307	1.689658	0.5372275
ALC1 KO PARP1 KO #1	exp3	exp3 ALC1 KO PARP1 KO #1	olaparib_300nM	3104	2.477121	3423.00	0.9068069	1.225043	0.7402242
ALC1 KO PARP1 KO #2	exp3	exp3 ALC1 KO PARP1 KO #2	olaparib_300nM	876	2.477121	879.75	0.9957374	1.234693	0.8064658
WT	exp4	exp4 WT	olaparib_300nM	2496	2.477121	2579.50	0.9676294	1.328293	0.7284759
PARP1 KO	exp4	exp4 PARP1 KO	olaparib_300nM	1468	2.477121	1427.75	1.0281912	1.151065	0.8932524
ALC1 KO #1	exp4	exp4 ALC1 KO #1	olaparib_300nM	1488	2.477121	1889.50	0.7875099	1.816617	0.4335036
ALC1 KO #2	exp4	exp4 ALC1 KO #2	olaparib_300nM	1768	2.477121	2034.00	0.8692232	1.750702	0.4964997
ALC1 KO #3	exp4	exp4 ALC1 KO #3	olaparib_300nM	1428	2.477121	1822.50	0.7835391	1.689658	0.4637265
ALC1 KO PARP1 KO #1	exp4	exp4 ALC1 KO PARP1 KO #1	olaparib_300nM	2864	2.477121	2918.00	0.9814942	1.225043	0.8011913
ALC1 KO PARP1 KO #2	exp4	exp4 ALC1 KO PARP1 KO #2	olaparib_300nM	1507	2.477121	1483.75	1.0156698	1.234693	0.8226094
WT	exp1	exp1 WT	olaparib_3000nM	1112	3.477121	1906.50	0.5832678	1.328293	0.4391108
PARP1 KO	exp1	exp1 PARP1 KO	olaparib_3000nM	1648	3.477121	2439.00	0.6756868	1.151065	0.5870103
ALC1 KO #1	exp1	exp1 ALC1 KO #1	olaparib_3000nM	560	3.477121	1221.00	0.4586405	1.816617	0.2524696
ALC1 KO #2	exp1	exp1 ALC1 KO #2	olaparib_3000nM	1117	3.477121	2277.25	0.4905039	1.750702	0.2801755
ALC1 KO #3	exp1	exp1 ALC1 KO #3	olaparib_3000nM	826	3.477121	1730.00	0.4774566	1.689658	0.2825760
ALC1 KO PARP1 KO #1	exp1	exp1 ALC1 KO PARP1 KO #1	olaparib_3000nM	1664	3.477121	2603.25	0.6392010	1.225043	0.5217782
ALC1 KO PARP1 KO #2	exp1	exp1 ALC1 KO PARP1 KO #2	olaparib_3000nM	2080	3.477121	2622.00	0.7932876	1.234693	0.6424980
WT	exp2	exp2 WT	olaparib_3000nM	1256	3.477121	2232.00	0.5627240	1.328293	0.4236445
PARP1 KO	exp2	exp2 PARP1 KO	olaparib_3000nM	1832	3.477121	2484.00	0.7375201	1.151065	0.6407287
ALC1 KO #1	exp2	exp2 ALC1 KO #1	olaparib_3000nM	636	3.477121	1503.00	0.4231537	1.816617	0.2329350
ALC1 KO #2	exp2	exp2 ALC1 KO #2	olaparib_3000nM	664	3.477121	1721.00	0.3858222	1.750702	0.2203814
ALC1 KO #3	exp2	exp2 ALC1 KO #3	olaparib_3000nM	832	3.477121	1712.50	0.4858394	1.689658	0.2875372
ALC1 KO PARP1 KO #1	exp2	exp2 ALC1 KO PARP1 KO #1	olaparib_3000nM	2864	3.477121	3636.00	0.7876788	1.225043	0.6429802
ALC1 KO PARP1 KO #2	exp2	exp2 ALC1 KO PARP1 KO #2	olaparib_3000nM	1416	3.477121	2128.00	0.6654135	1.234693	0.5389305
WT	exp3	exp3 WT	olaparib_3000nM	1424	3.477121	2274.00	0.6262093	1.328293	0.4714391
PARP1 KO	exp3	exp3 PARP1 KO	olaparib_3000nM	1219	3.477121	1595.00	0.7642633	1.151065	0.6639621
ALC1 KO #1	exp3	exp3 ALC1 KO #1	olaparib_3000nM	1078	3.477121	2329.50	0.4627602	1.816617	0.2547374
ALC1 KO #2	exp3	exp3 ALC1 KO #2	olaparib_3000nM	1352	3.477121	2423.00	0.5579860	1.750702	0.3187212
ALC1 KO #3	exp3	exp3 ALC1 KO #3	olaparib_3000nM	1344	3.477121	2406.00	0.5586035	1.689658	0.3306016
ALC1 KO PARP1 KO #1	exp3	exp3 ALC1 KO PARP1 KO #1	olaparib_3000nM	3072	3.477121	3423.00	0.8974584	1.225043	0.7325930
ALC1 KO PARP1 KO #2	exp3	exp3 ALC1 KO PARP1 KO #2	olaparib_3000nM	620	3.477121	879.75	0.7047457	1.234693	0.5707863
WT	exp4	exp4 WT	olaparib_3000nM	1742	3.477121	2579.50	0.6753247	1.328293	0.5084154
PARP1 KO	exp4	exp4 PARP1 KO	olaparib_3000nM	1058	3.477121	1427.75	0.7410261	1.151065	0.6437745
ALC1 KO #1	exp4	exp4 ALC1 KO #1	olaparib_3000nM	874	3.477121	1889.50	0.4625562	1.816617	0.2546251
ALC1 KO #2	exp4	exp4 ALC1 KO #2	olaparib_3000nM	1040	3.477121	2034.00	0.5113078	1.750702	0.2920587
ALC1 KO #3	exp4	exp4 ALC1 KO #3	olaparib_3000nM	822	3.477121	1822.50	0.4510288	1.689658	0.2669350
ALC1 KO PARP1 KO #1	exp4	exp4 ALC1 KO PARP1 KO #1	olaparib_3000nM	2328	3.477121	2918.00	0.7978067	1.225043	0.6512476
ALC1 KO PARP1 KO #2	exp4	exp4 ALC1 KO PARP1 KO #2	olaparib_3000nM	906	3.477121	1483.75	0.6106150	1.234693	0.4945482

Plot Data

library(ggplot2)

# raw data
ggplot(dataset, aes(x=Olaparib, y=Counts)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_smooth(method=lm, formula = y ~ poly(x, 2), se=FALSE, colour="#666666") +
        geom_point(aes(colour=Treatment, shape=Experiment), size=2) +        
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_shape_manual(values=15:19) +
        scale_color_manual(values=c("#999999","#0072B2","#CC79A7","#009E73"))

# NormCounts Linear
ggplot(dataset[dataset$genotype %in% levels(dataset$genotype)[c(1,2,6,7)],], aes(x=Olaparib, y=NormCounts)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(colour="#333333") +
        geom_smooth(method=lm, formula = y ~ x, se=TRUE, colour="#666666") +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)")

# NormCounts Quadratic
ggplot(dataset[dataset$genotype %in% levels(dataset$genotype)[c(1,2,6,7)],], aes(x=Olaparib, y=NormCounts)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(colour="#333333") +
        geom_smooth(method=lm, formula = y ~ poly(x, 2), se=TRUE, colour="#666666") +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") 

# NormCounts Cubic
ggplot(dataset[dataset$genotype %in% levels(dataset$genotype)[c(1,2,6,7)],], aes(x=Olaparib, y=NormCounts)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(colour="#333333") +
        geom_smooth(method=lm, formula = y ~ poly(x, 3), se=TRUE, colour="#666666") +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") 

# NormCounts2 Linear
ggplot(dataset[dataset$genotype %in% levels(dataset$genotype)[c(1,2,6,7)],], aes(x=Olaparib, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(colour="#333333") +
        geom_smooth(method=lm, formula = y ~ x, se=TRUE, colour="#666666") +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)")

# NormCounts2 Quadratic
ggplot(dataset[dataset$genotype %in% levels(dataset$genotype)[c(1,2,6,7)],], aes(x=Olaparib, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(colour="#333333") +
        geom_smooth(method=lm, formula = y ~ poly(x, 2), se=TRUE, colour="#666666") +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") 

# NormCounts2 Cubic
ggplot(dataset[dataset$genotype %in% levels(dataset$genotype)[c(1,2,6,7)],], aes(x=Olaparib, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(colour="#333333") +
        geom_smooth(method=lm, formula = y ~ poly(x, 3), se=TRUE, colour="#666666") +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") 

library(Cairo)

cairo_pdf("Figure1D_v1.pdf", width = 10, height = 4, family = "Arial")

datasubset <- dataset[dataset$genotype %in% levels(dataset$genotype)[c(1,2,3,7)],]
datasubset$genotype <- relevel(datasubset$genotype, ref = "WT")

ggplot(datasubset, aes(x=Olaparib, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), 
              axis.line = element_line(colour = "black"), text = element_text(size=14),
              panel.border = element_blank(), panel.background = element_blank()) +
        geom_point(aes(colour = genotype)) +
        geom_smooth(method=lm, formula = y ~ poly(x, 2), se=TRUE, aes(colour = genotype), fill='#DDDDDD', size=0.5) +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        ylab(label = "Normalized Counts") +
        scale_color_manual(values=c('#000000','#800000','#000080','#808080'))

dev.off()

## quartz_off_screen 
##                 2

cairo_pdf("Figure1D_v2.pdf", width = 6, height = 4, family = "Arial")

ggplot(datasubset, aes(x=Olaparib, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), 
              axis.line = element_line(colour = "black"), text = element_text(size=14),
              panel.border = element_blank(), panel.background = element_blank()) +
        geom_point(aes(colour = genotype), size=1.75) +
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=TRUE, 
                    aes(group = genotype, colour = genotype), fill='#DDDDDD', size=0.5) +
        #facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        ylab(label = "Normalized Counts") +
        scale_color_manual(values=c('#000000','#800000','#000080','#808080'))

dev.off()

## quartz_off_screen 
##                 2

cairo_pdf("FigureS1D.pdf", width = 6, height = 4, family = "Arial")

datasubset <- dataset[dataset$genotype %in% levels(dataset$genotype)[c(1,2,4,5,6)],]
datasubset$genotype <- relevel(datasubset$genotype, ref = "WT")

ggplot(datasubset, aes(x=Olaparib, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), 
              axis.line = element_line(colour = "black"), text = element_text(size=14),
              panel.border = element_blank(), panel.background = element_blank()) +
        geom_point(aes(colour = genotype), size=1.75) +
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=TRUE, 
                    aes(group = genotype, colour = genotype), fill='#DDDDDD', size=0.5) +
        #facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        ylab(label = "Normalized Counts") +
        scale_color_manual(values=c('#000000','#800000','#000080','#008080','#808080'))

dev.off()

## quartz_off_screen 
##                 2

Models

library(DHARMa)
library(lme4)
library(lmerTest)
library(bbmle)

Linear formula

fit1 <- lm(Counts ~ Experiment + Olaparib*genotype, data = dataset)
print(summary(fit1))

## 
## Call:
## lm(formula = Counts ~ Experiment + Olaparib * genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1292.19  -350.47    38.16   344.43  1059.70 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                          3086.986    231.702  13.323  < 2e-16 ***
## Experimentexp2                         88.214    132.029   0.668  0.50566    
## Experimentexp3                         75.893    132.029   0.575  0.56677    
## Experimentexp4                        -92.000    132.029  -0.697  0.48762    
## Olaparib                             -461.295     96.131  -4.799 5.93e-06 ***
## genotypePARP1 KO                     -703.721    307.079  -2.292  0.02413 *  
## genotypeALC1 KO #1                   -162.311    307.079  -0.529  0.59834    
## genotypeALC1 KO #2                    390.719    307.079   1.272  0.20635    
## genotypeALC1 KO #3                     -6.139    307.079  -0.020  0.98409    
## genotypeALC1 KO PARP1 KO #1           766.569    307.079   2.496  0.01427 *  
## genotypeALC1 KO PARP1 KO #2          -814.690    307.079  -2.653  0.00935 ** 
## Olaparib:genotypePARP1 KO             237.996    135.949   1.751  0.08324 .  
## Olaparib:genotypeALC1 KO #1          -188.358    135.949  -1.386  0.16914    
## Olaparib:genotypeALC1 KO #2          -282.536    135.949  -2.078  0.04038 *  
## Olaparib:genotypeALC1 KO #3          -174.456    135.949  -1.283  0.20253    
## Olaparib:genotypeALC1 KO PARP1 KO #1   70.240    135.949   0.517  0.60659    
## Olaparib:genotypeALC1 KO PARP1 KO #2  185.734    135.949   1.366  0.17510    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 494 on 95 degrees of freedom
## Multiple R-squared:  0.757,  Adjusted R-squared:  0.7161 
## F-statistic:  18.5 on 16 and 95 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit1))

## AIC:  1724.775

simres <- simulateResiduals(fittedModel = fit1)
plot(simres)

fit2 <- lm(NormCounts ~ Olaparib*genotype, data = dataset)
print(summary(fit2))

## 
## Call:
## lm(formula = NormCounts ~ Olaparib * genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.31437 -0.06435  0.00226  0.07579  0.32955 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                           1.38641    0.05086  27.257  < 2e-16 ***
## Olaparib                             -0.20799    0.02252  -9.236 5.50e-15 ***
## genotypePARP1 KO                     -0.18322    0.07193  -2.547 0.012420 *  
## genotypeALC1 KO #1                    0.32837    0.07193   4.565 1.45e-05 ***
## genotypeALC1 KO #2                    0.28288    0.07193   3.933 0.000157 ***
## genotypeALC1 KO #3                    0.24407    0.07193   3.393 0.000998 ***
## genotypeALC1 KO PARP1 KO #1          -0.14969    0.07193  -2.081 0.040041 *  
## genotypeALC1 KO PARP1 KO #2          -0.10276    0.07193  -1.429 0.156307    
## Olaparib:genotypePARP1 KO             0.09862    0.03185   3.097 0.002552 ** 
## Olaparib:genotypeALC1 KO #1          -0.17675    0.03185  -5.550 2.44e-07 ***
## Olaparib:genotypeALC1 KO #2          -0.15226    0.03185  -4.781 6.14e-06 ***
## Olaparib:genotypeALC1 KO #3          -0.13138    0.03185  -4.125 7.77e-05 ***
## Olaparib:genotypeALC1 KO PARP1 KO #1  0.08057    0.03185   2.530 0.012996 *  
## Olaparib:genotypeALC1 KO PARP1 KO #2  0.05531    0.03185   1.737 0.085552 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1157 on 98 degrees of freedom
## Multiple R-squared:  0.9075, Adjusted R-squared:  0.8953 
## F-statistic: 73.99 on 13 and 98 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit2))

## AIC:  -150.187

simres <- simulateResiduals(fittedModel = fit2)
plot(simres)

fit3 <- lm(NormCounts2 ~ Olaparib*genotype, data = dataset)
print(summary(fit3))

## 
## Call:
## lm(formula = NormCounts2 ~ Olaparib * genotype, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.173053 -0.051127  0.001775  0.049137  0.188241 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                           1.043751   0.033393  31.257  < 2e-16 ***
## Olaparib                             -0.156582   0.014784 -10.592  < 2e-16 ***
## genotypePARP1 KO                      0.001535   0.047225   0.032  0.97414    
## genotypeALC1 KO #1                   -0.099811   0.047225  -2.114  0.03709 *  
## genotypeALC1 KO #2                   -0.090257   0.047225  -1.911  0.05890 .  
## genotypeALC1 KO #3                   -0.078773   0.047225  -1.668  0.09850 .  
## genotypeALC1 KO PARP1 KO #1          -0.034224   0.047225  -0.725  0.47036    
## genotypeALC1 KO PARP1 KO #2          -0.004103   0.047225  -0.087  0.93094    
## Olaparib:genotypePARP1 KO             0.061566   0.020907   2.945  0.00404 ** 
## Olaparib:genotypeALC1 KO #1          -0.055204   0.020907  -2.640  0.00964 ** 
## Olaparib:genotypeALC1 KO #2          -0.049192   0.020907  -2.353  0.02063 *  
## Olaparib:genotypeALC1 KO #3          -0.044265   0.020907  -2.117  0.03677 *  
## Olaparib:genotypeALC1 KO PARP1 KO #1  0.052575   0.020907   2.515  0.01354 *  
## Olaparib:genotypeALC1 KO PARP1 KO #2  0.032928   0.020907   1.575  0.11849    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07597 on 98 degrees of freedom
## Multiple R-squared:  0.9217, Adjusted R-squared:  0.9113 
## F-statistic:  88.7 on 13 and 98 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit3))

## AIC:  -244.4496

simres <- simulateResiduals(fittedModel = fit3)
plot(simres)

fit4 <- lmer(Counts ~ Olaparib*genotype + (1|UID), data = dataset)
print(summary(fit4))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ Olaparib * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 1452.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.0250 -0.4734  0.0399  0.5726  1.5679 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 216177   464.9   
##  Residual              57192   239.1   
## Number of obs: 112, groups:  UID, 28
## 
## Fixed effects:
##                                      Estimate Std. Error       df t value
## (Intercept)                          3105.013    255.135   26.680  12.170
## Olaparib                             -461.295     46.537   77.000  -9.912
## genotypePARP1 KO                     -703.721    360.815   26.680  -1.950
## genotypeALC1 KO #1                   -162.311    360.815   26.680  -0.450
## genotypeALC1 KO #2                    390.719    360.815   26.680   1.083
## genotypeALC1 KO #3                     -6.139    360.815   26.680  -0.017
## genotypeALC1 KO PARP1 KO #1           766.569    360.815   26.680   2.125
## genotypeALC1 KO PARP1 KO #2          -814.690    360.815   26.680  -2.258
## Olaparib:genotypePARP1 KO             237.996     65.813   77.000   3.616
## Olaparib:genotypeALC1 KO #1          -188.358     65.813   77.000  -2.862
## Olaparib:genotypeALC1 KO #2          -282.536     65.813   77.000  -4.293
## Olaparib:genotypeALC1 KO #3          -174.456     65.813   77.000  -2.651
## Olaparib:genotypeALC1 KO PARP1 KO #1   70.240     65.813   77.000   1.067
## Olaparib:genotypeALC1 KO PARP1 KO #2  185.734     65.813   77.000   2.822
##                                      Pr(>|t|)    
## (Intercept)                          2.13e-12 ***
## Olaparib                             2.12e-15 ***
## genotypePARP1 KO                     0.061712 .  
## genotypeALC1 KO #1                   0.656457    
## genotypeALC1 KO #2                   0.288552    
## genotypeALC1 KO #3                   0.986552    
## genotypeALC1 KO PARP1 KO #1          0.043042 *  
## genotypeALC1 KO PARP1 KO #2          0.032346 *  
## Olaparib:genotypePARP1 KO            0.000532 ***
## Olaparib:genotypeALC1 KO #1          0.005419 ** 
## Olaparib:genotypeALC1 KO #2          5.08e-05 ***
## Olaparib:genotypeALC1 KO #3          0.009744 ** 
## Olaparib:genotypeALC1 KO PARP1 KO #1 0.289192    
## Olaparib:genotypeALC1 KO PARP1 KO #2 0.006067 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

cat("AIC: ", AIC(fit4))

## AIC:  1484.326

simres <- simulateResiduals(fittedModel = fit4)
plot(simres)

Quadratic formula

fit5 <- lm(Counts ~ Experiment + poly(Olaparib, 2)*genotype, data = dataset)
print(summary(fit5))

## 
## Call:
## lm(formula = Counts ~ Experiment + poly(Olaparib, 2) * genotype, 
##     data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1221.99  -313.25    -4.75   325.69  1129.90 
## 
## Coefficients:
##                                                Estimate Std. Error t value
## (Intercept)                                     2229.97     148.27  15.040
## Experimentexp2                                    88.21     132.61   0.665
## Experimentexp3                                    75.89     132.61   0.572
## Experimentexp4                                   -92.00     132.61  -0.694
## poly(Olaparib, 2)1                             -6271.89    1312.81  -4.777
## poly(Olaparib, 2)2                             -1344.84    1312.81  -1.024
## genotypePARP1 KO                                -261.56     175.43  -1.491
## genotypeALC1 KO #1                              -512.25     175.43  -2.920
## genotypeALC1 KO #2                              -134.19     175.43  -0.765
## genotypeALC1 KO #3                              -330.25     175.43  -1.883
## genotypeALC1 KO PARP1 KO #1                      897.06     175.43   5.113
## genotypeALC1 KO PARP1 KO #2                     -469.62     175.43  -2.677
## poly(Olaparib, 2)1:genotypePARP1 KO             3235.85    1856.59   1.743
## poly(Olaparib, 2)2:genotypePARP1 KO              -95.58    1856.59  -0.051
## poly(Olaparib, 2)1:genotypeALC1 KO #1          -2560.97    1856.59  -1.379
## poly(Olaparib, 2)2:genotypeALC1 KO #1           2809.89    1856.59   1.513
## poly(Olaparib, 2)1:genotypeALC1 KO #2          -3841.44    1856.59  -2.069
## poly(Olaparib, 2)2:genotypeALC1 KO #2           3027.78    1856.59   1.631
## poly(Olaparib, 2)1:genotypeALC1 KO #3          -2371.95    1856.59  -1.278
## poly(Olaparib, 2)2:genotypeALC1 KO #3           2323.84    1856.59   1.252
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #1   955.00    1856.59   0.514
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #1   977.10    1856.59   0.526
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #2  2525.30    1856.59   1.360
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #2   528.20    1856.59   0.285
##                                                Pr(>|t|)    
## (Intercept)                                     < 2e-16 ***
## Experimentexp2                                  0.50766    
## Experimentexp3                                  0.56859    
## Experimentexp4                                  0.48967    
## poly(Olaparib, 2)1                             7.05e-06 ***
## poly(Olaparib, 2)2                              0.30845    
## genotypePARP1 KO                                0.13955    
## genotypeALC1 KO #1                              0.00445 ** 
## genotypeALC1 KO #2                              0.44638    
## genotypeALC1 KO #3                              0.06307 .  
## genotypeALC1 KO PARP1 KO #1                    1.83e-06 ***
## genotypeALC1 KO PARP1 KO #2                     0.00886 ** 
## poly(Olaparib, 2)1:genotypePARP1 KO             0.08484 .  
## poly(Olaparib, 2)2:genotypePARP1 KO             0.95906    
## poly(Olaparib, 2)1:genotypeALC1 KO #1           0.17127    
## poly(Olaparib, 2)2:genotypeALC1 KO #1           0.13374    
## poly(Olaparib, 2)1:genotypeALC1 KO #2           0.04147 *  
## poly(Olaparib, 2)2:genotypeALC1 KO #2           0.10650    
## poly(Olaparib, 2)1:genotypeALC1 KO #3           0.20476    
## poly(Olaparib, 2)2:genotypeALC1 KO #3           0.21401    
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #1  0.60828    
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #1  0.60001    
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #2  0.17725    
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #2  0.77670    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 496.2 on 88 degrees of freedom
## Multiple R-squared:  0.7729, Adjusted R-squared:  0.7135 
## F-statistic: 13.02 on 23 and 88 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit5))

## AIC:  1731.193

simres <- simulateResiduals(fittedModel = fit5)
plot(simres)

fit6 <- lm(NormCounts ~ poly(Olaparib, 2)*genotype, data = dataset)
print(summary(fit6))

## 
## Call:
## lm(formula = NormCounts ~ poly(Olaparib, 2) * genotype, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.224412 -0.037884 -0.002421  0.044466  0.256725 
## 
## Coefficients:
##                                                  Estimate Std. Error t value
## (Intercept)                                     1.000e+00  2.395e-02  41.754
## poly(Olaparib, 2)1                             -2.828e+00  2.535e-01 -11.157
## poly(Olaparib, 2)2                             -6.107e-01  2.535e-01  -2.409
## genotypePARP1 KO                                3.457e-16  3.387e-02   0.000
## genotypeALC1 KO #1                              3.350e-16  3.387e-02   0.000
## genotypeALC1 KO #2                              1.906e-16  3.387e-02   0.000
## genotypeALC1 KO #3                              1.399e-16  3.387e-02   0.000
## genotypeALC1 KO PARP1 KO #1                     2.704e-16  3.387e-02   0.000
## genotypeALC1 KO PARP1 KO #2                     2.969e-16  3.387e-02   0.000
## poly(Olaparib, 2)1:genotypePARP1 KO             1.341e+00  3.585e-01   3.741
## poly(Olaparib, 2)2:genotypePARP1 KO            -1.201e-01  3.585e-01  -0.335
## poly(Olaparib, 2)1:genotypeALC1 KO #1          -2.403e+00  3.585e-01  -6.704
## poly(Olaparib, 2)2:genotypeALC1 KO #1           1.622e+00  3.585e-01   4.525
## poly(Olaparib, 2)1:genotypeALC1 KO #2          -2.070e+00  3.585e-01  -5.775
## poly(Olaparib, 2)2:genotypeALC1 KO #2           1.458e+00  3.585e-01   4.067
## poly(Olaparib, 2)1:genotypeALC1 KO #3          -1.786e+00  3.585e-01  -4.983
## poly(Olaparib, 2)2:genotypeALC1 KO #3           1.196e+00  3.585e-01   3.336
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #1  1.096e+00  3.585e-01   3.056
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #1  4.771e-01  3.585e-01   1.331
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #2  7.520e-01  3.585e-01   2.098
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #2  3.420e-02  3.585e-01   0.095
##                                                Pr(>|t|)    
## (Intercept)                                     < 2e-16 ***
## poly(Olaparib, 2)1                              < 2e-16 ***
## poly(Olaparib, 2)2                             0.017990 *  
## genotypePARP1 KO                               1.000000    
## genotypeALC1 KO #1                             1.000000    
## genotypeALC1 KO #2                             1.000000    
## genotypeALC1 KO #3                             1.000000    
## genotypeALC1 KO PARP1 KO #1                    1.000000    
## genotypeALC1 KO PARP1 KO #2                    1.000000    
## poly(Olaparib, 2)1:genotypePARP1 KO            0.000321 ***
## poly(Olaparib, 2)2:genotypePARP1 KO            0.738439    
## poly(Olaparib, 2)1:genotypeALC1 KO #1          1.66e-09 ***
## poly(Olaparib, 2)2:genotypeALC1 KO #1          1.82e-05 ***
## poly(Olaparib, 2)1:genotypeALC1 KO #2          1.06e-07 ***
## poly(Olaparib, 2)2:genotypeALC1 KO #2          0.000101 ***
## poly(Olaparib, 2)1:genotypeALC1 KO #3          2.97e-06 ***
## poly(Olaparib, 2)2:genotypeALC1 KO #3          0.001232 ** 
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #1 0.002942 ** 
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #1 0.186540    
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #2 0.038674 *  
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #2 0.924202    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0958 on 91 degrees of freedom
## Multiple R-squared:  0.9412, Adjusted R-squared:  0.9282 
## F-statistic: 72.77 on 20 and 91 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit6))

## AIC:  -186.8034

simres <- simulateResiduals(fittedModel = fit6)
plot(simres)

fit7 <- lm(NormCounts2 ~ poly(Olaparib, 2)*genotype, data = dataset)
print(summary(fit7))

## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 2) * genotype, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.132815 -0.030932 -0.001158  0.033036  0.146641 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                     0.752846   0.015507  48.547
## poly(Olaparib, 2)1                             -2.128936   0.164116 -12.972
## poly(Olaparib, 2)2                             -0.459768   0.164116  -2.801
## genotypePARP1 KO                                0.115915   0.021931   5.285
## genotypeALC1 KO #1                             -0.202372   0.021931  -9.228
## genotypeALC1 KO #2                             -0.181647   0.021931  -8.283
## genotypeALC1 KO #3                             -0.161010   0.021931  -7.342
## genotypeALC1 KO PARP1 KO #1                     0.063452   0.021931   2.893
## genotypeALC1 KO PARP1 KO #2                     0.057072   0.021931   2.602
## poly(Olaparib, 2)1:genotypePARP1 KO             0.837073   0.232095   3.607
## poly(Olaparib, 2)2:genotypePARP1 KO            -0.175094   0.232095  -0.754
## poly(Olaparib, 2)1:genotypeALC1 KO #1          -0.750574   0.232095  -3.234
## poly(Olaparib, 2)2:genotypeALC1 KO #1           1.016525   0.232095   4.380
## poly(Olaparib, 2)1:genotypeALC1 KO #2          -0.668821   0.232095  -2.882
## poly(Olaparib, 2)2:genotypeALC1 KO #2           0.943698   0.232095   4.066
## poly(Olaparib, 2)1:genotypeALC1 KO #3          -0.601835   0.232095  -2.593
## poly(Olaparib, 2)2:genotypeALC1 KO #3           0.806003   0.232095   3.473
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #1  0.714823   0.232095   3.080
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #1  0.350681   0.232095   1.511
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #2  0.447700   0.232095   1.929
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #2 -0.007157   0.232095  -0.031
##                                                Pr(>|t|)    
## (Intercept)                                     < 2e-16 ***
## poly(Olaparib, 2)1                              < 2e-16 ***
## poly(Olaparib, 2)2                             0.006214 ** 
## genotypePARP1 KO                               8.54e-07 ***
## genotypeALC1 KO #1                             1.05e-14 ***
## genotypeALC1 KO #2                             9.92e-13 ***
## genotypeALC1 KO #3                             8.63e-11 ***
## genotypeALC1 KO PARP1 KO #1                    0.004771 ** 
## genotypeALC1 KO PARP1 KO #2                    0.010808 *  
## poly(Olaparib, 2)1:genotypePARP1 KO            0.000506 ***
## poly(Olaparib, 2)2:genotypePARP1 KO            0.452552    
## poly(Olaparib, 2)1:genotypeALC1 KO #1          0.001702 ** 
## poly(Olaparib, 2)2:genotypeALC1 KO #1          3.17e-05 ***
## poly(Olaparib, 2)1:genotypeALC1 KO #2          0.004934 ** 
## poly(Olaparib, 2)2:genotypeALC1 KO #2          0.000101 ***
## poly(Olaparib, 2)1:genotypeALC1 KO #3          0.011084 *  
## poly(Olaparib, 2)2:genotypeALC1 KO #3          0.000790 ***
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #1 0.002738 ** 
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #1 0.134268    
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #2 0.056853 .  
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #2 0.975469    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06203 on 91 degrees of freedom
## Multiple R-squared:  0.9515, Adjusted R-squared:  0.9409 
## F-statistic: 89.28 on 20 and 91 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit7))

## AIC:  -284.1646

simres <- simulateResiduals(fittedModel = fit7)
plot(simres)

fit8 <- lmer(Counts ~ poly(Olaparib, 2)*genotype + (1|UID), data = dataset)
print(summary(fit8))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Olaparib, 2) * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 1282.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.70753 -0.56360  0.09369  0.52591  1.63379 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 220167   469.2   
##  Residual              41233   203.1   
## Number of obs: 112, groups:  UID, 28
## 
## Fixed effects:
##                                                Estimate Std. Error       df
## (Intercept)                                     2248.00     240.04    21.00
## poly(Olaparib, 2)1                             -6271.89     537.24    70.00
## poly(Olaparib, 2)2                             -1344.84     537.24    70.00
## genotypePARP1 KO                                -261.56     339.47    21.00
## genotypeALC1 KO #1                              -512.25     339.47    21.00
## genotypeALC1 KO #2                              -134.19     339.47    21.00
## genotypeALC1 KO #3                              -330.25     339.47    21.00
## genotypeALC1 KO PARP1 KO #1                      897.06     339.47    21.00
## genotypeALC1 KO PARP1 KO #2                     -469.63     339.47    21.00
## poly(Olaparib, 2)1:genotypePARP1 KO             3235.85     759.78    70.00
## poly(Olaparib, 2)2:genotypePARP1 KO              -95.58     759.78    70.00
## poly(Olaparib, 2)1:genotypeALC1 KO #1          -2560.97     759.78    70.00
## poly(Olaparib, 2)2:genotypeALC1 KO #1           2809.89     759.78    70.00
## poly(Olaparib, 2)1:genotypeALC1 KO #2          -3841.44     759.78    70.00
## poly(Olaparib, 2)2:genotypeALC1 KO #2           3027.78     759.78    70.00
## poly(Olaparib, 2)1:genotypeALC1 KO #3          -2371.95     759.78    70.00
## poly(Olaparib, 2)2:genotypeALC1 KO #3           2323.84     759.78    70.00
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #1   955.00     759.78    70.00
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #1   977.10     759.78    70.00
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #2  2525.30     759.78    70.00
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #2   528.20     759.78    70.00
##                                                t value Pr(>|t|)    
## (Intercept)                                      9.365 6.03e-09 ***
## poly(Olaparib, 2)1                             -11.674  < 2e-16 ***
## poly(Olaparib, 2)2                              -2.503 0.014643 *  
## genotypePARP1 KO                                -0.771 0.449579    
## genotypeALC1 KO #1                              -1.509 0.146202    
## genotypeALC1 KO #2                              -0.395 0.696616    
## genotypeALC1 KO #3                              -0.973 0.341701    
## genotypeALC1 KO PARP1 KO #1                      2.643 0.015224 *  
## genotypeALC1 KO PARP1 KO #2                     -1.383 0.181067    
## poly(Olaparib, 2)1:genotypePARP1 KO              4.259 6.28e-05 ***
## poly(Olaparib, 2)2:genotypePARP1 KO             -0.126 0.900254    
## poly(Olaparib, 2)1:genotypeALC1 KO #1           -3.371 0.001223 ** 
## poly(Olaparib, 2)2:genotypeALC1 KO #1            3.698 0.000428 ***
## poly(Olaparib, 2)1:genotypeALC1 KO #2           -5.056 3.29e-06 ***
## poly(Olaparib, 2)2:genotypeALC1 KO #2            3.985 0.000163 ***
## poly(Olaparib, 2)1:genotypeALC1 KO #3           -3.122 0.002612 ** 
## poly(Olaparib, 2)2:genotypeALC1 KO #3            3.059 0.003150 ** 
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #1   1.257 0.212952    
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #1   1.286 0.202669    
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO #2   3.324 0.001416 ** 
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO #2   0.695 0.489228    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

cat("AIC: ", AIC(fit8))

## AIC:  1328.72

simres <- simulateResiduals(fittedModel = fit8)
plot(simres)

Cubic formula

fit9 <- lm(Counts ~ Experiment + poly(Olaparib, 3)*genotype, data = dataset)
print(summary(fit9))

## 
## Call:
## lm(formula = Counts ~ Experiment + poly(Olaparib, 3) * genotype, 
##     data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1218.87  -309.44    -8.67   323.93  1133.03 
## 
## Coefficients:
##                                                Estimate Std. Error t value
## (Intercept)                                     2229.97     153.22  14.554
## Experimentexp2                                    88.21     137.04   0.644
## Experimentexp3                                    75.89     137.04   0.554
## Experimentexp4                                   -92.00     137.04  -0.671
## poly(Olaparib, 3)1                             -6271.89    1356.64  -4.623
## poly(Olaparib, 3)2                             -1344.84    1356.64  -0.991
## poly(Olaparib, 3)3                               367.99    1356.64   0.271
## genotypePARP1 KO                                -261.56     181.29  -1.443
## genotypeALC1 KO #1                              -512.25     181.29  -2.826
## genotypeALC1 KO #2                              -134.19     181.29  -0.740
## genotypeALC1 KO #3                              -330.25     181.29  -1.822
## genotypeALC1 KO PARP1 KO #1                      897.06     181.29   4.948
## genotypeALC1 KO PARP1 KO #2                     -469.62     181.29  -2.590
## poly(Olaparib, 3)1:genotypePARP1 KO             3235.85    1918.58   1.687
## poly(Olaparib, 3)2:genotypePARP1 KO              -95.58    1918.58  -0.050
## poly(Olaparib, 3)3:genotypePARP1 KO            -1186.25    1918.58  -0.618
## poly(Olaparib, 3)1:genotypeALC1 KO #1          -2560.97    1918.58  -1.335
## poly(Olaparib, 3)2:genotypeALC1 KO #1           2809.89    1918.58   1.465
## poly(Olaparib, 3)3:genotypeALC1 KO #1          -1272.78    1918.58  -0.663
## poly(Olaparib, 3)1:genotypeALC1 KO #2          -3841.44    1918.58  -2.002
## poly(Olaparib, 3)2:genotypeALC1 KO #2           3027.78    1918.58   1.578
## poly(Olaparib, 3)3:genotypeALC1 KO #2          -1095.97    1918.58  -0.571
## poly(Olaparib, 3)1:genotypeALC1 KO #3          -2371.95    1918.58  -1.236
## poly(Olaparib, 3)2:genotypeALC1 KO #3           2323.84    1918.58   1.211
## poly(Olaparib, 3)3:genotypeALC1 KO #3           -998.31    1918.58  -0.520
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #1   955.00    1918.58   0.498
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #1   977.10    1918.58   0.509
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #1  -220.56    1918.58  -0.115
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #2  2525.30    1918.58   1.316
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #2   528.20    1918.58   0.275
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #2  -252.01    1918.58  -0.131
##                                                Pr(>|t|)    
## (Intercept)                                     < 2e-16 ***
## Experimentexp2                                  0.52159    
## Experimentexp3                                  0.58125    
## Experimentexp4                                  0.50392    
## poly(Olaparib, 3)1                             1.41e-05 ***
## poly(Olaparib, 3)2                              0.32449    
## poly(Olaparib, 3)3                              0.78689    
## genotypePARP1 KO                                0.15294    
## genotypeALC1 KO #1                              0.00594 ** 
## genotypeALC1 KO #2                              0.46133    
## genotypeALC1 KO #3                              0.07220 .  
## genotypeALC1 KO PARP1 KO #1                    4.00e-06 ***
## genotypeALC1 KO PARP1 KO #2                     0.01136 *  
## poly(Olaparib, 3)1:genotypePARP1 KO             0.09553 .  
## poly(Olaparib, 3)2:genotypePARP1 KO             0.96039    
## poly(Olaparib, 3)3:genotypePARP1 KO             0.53812    
## poly(Olaparib, 3)1:genotypeALC1 KO #1           0.18567    
## poly(Olaparib, 3)2:genotypeALC1 KO #1           0.14691    
## poly(Olaparib, 3)3:genotypeALC1 KO #1           0.50896    
## poly(Olaparib, 3)1:genotypeALC1 KO #2           0.04861 *  
## poly(Olaparib, 3)2:genotypeALC1 KO #2           0.11843    
## poly(Olaparib, 3)3:genotypeALC1 KO #2           0.56942    
## poly(Olaparib, 3)1:genotypeALC1 KO #3           0.21992    
## poly(Olaparib, 3)2:genotypeALC1 KO #3           0.22933    
## poly(Olaparib, 3)3:genotypeALC1 KO #3           0.60425    
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #1  0.62000    
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #1  0.61194    
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #1  0.90876    
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #2  0.19181    
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #2  0.78378    
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #2  0.89582    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 512.8 on 81 degrees of freedom
## Multiple R-squared:  0.7768, Adjusted R-squared:  0.6941 
## F-statistic: 9.395 on 30 and 81 DF,  p-value: 5.106e-16

cat("AIC: ", AIC(fit9))

## AIC:  1743.267

simres <- simulateResiduals(fittedModel = fit9)
plot(simres)

fit10 <- lm(NormCounts ~ poly(Olaparib, 3)*genotype, data = dataset)
print(summary(fit10))

## 
## Call:
## lm(formula = NormCounts ~ poly(Olaparib, 3) * genotype, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.233299 -0.046833  0.007519  0.051494  0.248136 
## 
## Coefficients:
##                                                  Estimate Std. Error t value
## (Intercept)                                     1.000e+00  2.335e-02  42.835
## poly(Olaparib, 3)1                             -2.828e+00  2.471e-01 -11.446
## poly(Olaparib, 3)2                             -6.107e-01  2.471e-01  -2.472
## poly(Olaparib, 3)3                              2.086e-01  2.471e-01   0.844
## genotypePARP1 KO                                2.267e-16  3.302e-02   0.000
## genotypeALC1 KO #1                              9.707e-17  3.302e-02   0.000
## genotypeALC1 KO #2                              4.795e-16  3.302e-02   0.000
## genotypeALC1 KO #3                              4.313e-16  3.302e-02   0.000
## genotypeALC1 KO PARP1 KO #1                     3.832e-16  3.302e-02   0.000
## genotypeALC1 KO PARP1 KO #2                     4.676e-16  3.302e-02   0.000
## poly(Olaparib, 3)1:genotypePARP1 KO             1.341e+00  3.494e-01   3.837
## poly(Olaparib, 3)2:genotypePARP1 KO            -1.201e-01  3.494e-01  -0.344
## poly(Olaparib, 3)3:genotypePARP1 KO            -6.058e-01  3.494e-01  -1.734
## poly(Olaparib, 3)1:genotypeALC1 KO #1          -2.403e+00  3.494e-01  -6.878
## poly(Olaparib, 3)2:genotypeALC1 KO #1           1.622e+00  3.494e-01   4.643
## poly(Olaparib, 3)3:genotypeALC1 KO #1          -7.619e-01  3.494e-01  -2.181
## poly(Olaparib, 3)1:genotypeALC1 KO #2          -2.070e+00  3.494e-01  -5.925
## poly(Olaparib, 3)2:genotypeALC1 KO #2           1.458e+00  3.494e-01   4.173
## poly(Olaparib, 3)3:genotypeALC1 KO #2          -5.276e-01  3.494e-01  -1.510
## poly(Olaparib, 3)1:genotypeALC1 KO #3          -1.786e+00  3.494e-01  -5.112
## poly(Olaparib, 3)2:genotypeALC1 KO #3           1.196e+00  3.494e-01   3.422
## poly(Olaparib, 3)3:genotypeALC1 KO #3          -5.387e-01  3.494e-01  -1.542
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #1  1.096e+00  3.494e-01   3.135
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #1  4.771e-01  3.494e-01   1.365
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #1 -2.018e-01  3.494e-01  -0.578
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #2  7.520e-01  3.494e-01   2.152
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #2  3.420e-02  3.494e-01   0.098
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #2 -2.311e-01  3.494e-01  -0.661
##                                                Pr(>|t|)    
## (Intercept)                                     < 2e-16 ***
## poly(Olaparib, 3)1                              < 2e-16 ***
## poly(Olaparib, 3)2                             0.015461 *  
## poly(Olaparib, 3)3                             0.400861    
## genotypePARP1 KO                               1.000000    
## genotypeALC1 KO #1                             1.000000    
## genotypeALC1 KO #2                             1.000000    
## genotypeALC1 KO #3                             1.000000    
## genotypeALC1 KO PARP1 KO #1                    1.000000    
## genotypeALC1 KO PARP1 KO #2                    1.000000    
## poly(Olaparib, 3)1:genotypePARP1 KO            0.000240 ***
## poly(Olaparib, 3)2:genotypePARP1 KO            0.731994    
## poly(Olaparib, 3)3:genotypePARP1 KO            0.086645 .  
## poly(Olaparib, 3)1:genotypeALC1 KO #1          1.01e-09 ***
## poly(Olaparib, 3)2:genotypeALC1 KO #1          1.26e-05 ***
## poly(Olaparib, 3)3:genotypeALC1 KO #1          0.032005 *  
## poly(Olaparib, 3)1:genotypeALC1 KO #2          6.66e-08 ***
## poly(Olaparib, 3)2:genotypeALC1 KO #2          7.31e-05 ***
## poly(Olaparib, 3)3:genotypeALC1 KO #2          0.134765    
## poly(Olaparib, 3)1:genotypeALC1 KO #3          1.97e-06 ***
## poly(Olaparib, 3)2:genotypeALC1 KO #3          0.000962 ***
## poly(Olaparib, 3)3:genotypeALC1 KO #3          0.126893    
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #1 0.002366 ** 
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #1 0.175778    
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #1 0.565075    
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #2 0.034239 *  
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #2 0.922264    
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #2 0.510194    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.09338 on 84 degrees of freedom
## Multiple R-squared:  0.9484, Adjusted R-squared:  0.9318 
## F-statistic: 57.17 on 27 and 84 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit10))

## AIC:  -187.4945

simres <- simulateResiduals(fittedModel = fit10)
plot(simres)

fit11 <- lm(NormCounts2 ~ poly(Olaparib, 3)*genotype, data = dataset)
print(summary(fit11))

## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * genotype, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.138074 -0.031185  0.005882  0.035369  0.141735 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                     0.752846   0.015092  49.885
## poly(Olaparib, 3)1                             -2.128936   0.159716 -13.329
## poly(Olaparib, 3)2                             -0.459768   0.159716  -2.879
## poly(Olaparib, 3)3                              0.157055   0.159716   0.983
## genotypePARP1 KO                                0.115915   0.021343   5.431
## genotypeALC1 KO #1                             -0.202372   0.021343  -9.482
## genotypeALC1 KO #2                             -0.181647   0.021343  -8.511
## genotypeALC1 KO #3                             -0.161010   0.021343  -7.544
## genotypeALC1 KO PARP1 KO #1                     0.063452   0.021343   2.973
## genotypeALC1 KO PARP1 KO #2                     0.057072   0.021343   2.674
## poly(Olaparib, 3)1:genotypePARP1 KO             0.837073   0.225873   3.706
## poly(Olaparib, 3)2:genotypePARP1 KO            -0.175094   0.225873  -0.775
## poly(Olaparib, 3)3:genotypePARP1 KO            -0.502073   0.225873  -2.223
## poly(Olaparib, 3)1:genotypeALC1 KO #1          -0.750574   0.225873  -3.323
## poly(Olaparib, 3)2:genotypeALC1 KO #1           1.016525   0.225873   4.500
## poly(Olaparib, 3)3:genotypeALC1 KO #1          -0.461642   0.225873  -2.044
## poly(Olaparib, 3)1:genotypeALC1 KO #2          -0.668821   0.225873  -2.961
## poly(Olaparib, 3)2:genotypeALC1 KO #2           0.943698   0.225873   4.178
## poly(Olaparib, 3)3:genotypeALC1 KO #2          -0.339283   0.225873  -1.502
## poly(Olaparib, 3)1:genotypeALC1 KO #3          -0.601835   0.225873  -2.664
## poly(Olaparib, 3)2:genotypeALC1 KO #3           0.806003   0.225873   3.568
## poly(Olaparib, 3)3:genotypeALC1 KO #3          -0.352408   0.225873  -1.560
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #1  0.714823   0.225873   3.165
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #1  0.350681   0.225873   1.553
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #1 -0.151507   0.225873  -0.671
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #2  0.447700   0.225873   1.982
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #2 -0.007157   0.225873  -0.032
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #2 -0.175250   0.225873  -0.776
##                                                Pr(>|t|)    
## (Intercept)                                     < 2e-16 ***
## poly(Olaparib, 3)1                              < 2e-16 ***
## poly(Olaparib, 3)2                             0.005064 ** 
## poly(Olaparib, 3)3                             0.328265    
## genotypePARP1 KO                               5.35e-07 ***
## genotypeALC1 KO #1                             6.36e-15 ***
## genotypeALC1 KO #2                             5.73e-13 ***
## genotypeALC1 KO #3                             4.92e-11 ***
## genotypeALC1 KO PARP1 KO #1                    0.003848 ** 
## genotypeALC1 KO PARP1 KO #2                    0.009004 ** 
## poly(Olaparib, 3)1:genotypePARP1 KO            0.000376 ***
## poly(Olaparib, 3)2:genotypePARP1 KO            0.440405    
## poly(Olaparib, 3)3:genotypePARP1 KO            0.028915 *  
## poly(Olaparib, 3)1:genotypeALC1 KO #1          0.001321 ** 
## poly(Olaparib, 3)2:genotypeALC1 KO #1          2.16e-05 ***
## poly(Olaparib, 3)3:genotypeALC1 KO #1          0.044105 *  
## poly(Olaparib, 3)1:genotypeALC1 KO #2          0.003985 ** 
## poly(Olaparib, 3)2:genotypeALC1 KO #2          7.16e-05 ***
## poly(Olaparib, 3)3:genotypeALC1 KO #2          0.136822    
## poly(Olaparib, 3)1:genotypeALC1 KO #3          0.009243 ** 
## poly(Olaparib, 3)2:genotypeALC1 KO #3          0.000597 ***
## poly(Olaparib, 3)3:genotypeALC1 KO #3          0.122472    
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #1 0.002162 ** 
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #1 0.124288    
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #1 0.504213    
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #2 0.050739 .  
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #2 0.974799    
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #2 0.439999    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06037 on 84 degrees of freedom
## Multiple R-squared:  0.9576, Adjusted R-squared:  0.944 
## F-statistic: 70.28 on 27 and 84 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit11))

## AIC:  -285.2159

simres <- simulateResiduals(fittedModel = fit11)
plot(simres)

fit12 <- lmer(Counts ~ poly(Olaparib, 3)*genotype + (1|UID), data = dataset)
print(summary(fit12))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Olaparib, 3) * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 1172.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.62206 -0.45619 -0.00004  0.51995  1.66007 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 220488   469.6   
##  Residual              39950   199.9   
## Number of obs: 112, groups:  UID, 28
## 
## Fixed effects:
##                                                Estimate Std. Error       df
## (Intercept)                                     2248.00     240.04    21.00
## poly(Olaparib, 3)1                             -6271.89     528.82    63.00
## poly(Olaparib, 3)2                             -1344.84     528.82    63.00
## poly(Olaparib, 3)3                               367.99     528.82    63.00
## genotypePARP1 KO                                -261.56     339.47    21.00
## genotypeALC1 KO #1                              -512.25     339.47    21.00
## genotypeALC1 KO #2                              -134.19     339.47    21.00
## genotypeALC1 KO #3                              -330.25     339.47    21.00
## genotypeALC1 KO PARP1 KO #1                      897.06     339.47    21.00
## genotypeALC1 KO PARP1 KO #2                     -469.63     339.47    21.00
## poly(Olaparib, 3)1:genotypePARP1 KO             3235.85     747.87    63.00
## poly(Olaparib, 3)2:genotypePARP1 KO              -95.58     747.87    63.00
## poly(Olaparib, 3)3:genotypePARP1 KO            -1186.25     747.87    63.00
## poly(Olaparib, 3)1:genotypeALC1 KO #1          -2560.97     747.87    63.00
## poly(Olaparib, 3)2:genotypeALC1 KO #1           2809.89     747.87    63.00
## poly(Olaparib, 3)3:genotypeALC1 KO #1          -1272.78     747.87    63.00
## poly(Olaparib, 3)1:genotypeALC1 KO #2          -3841.44     747.87    63.00
## poly(Olaparib, 3)2:genotypeALC1 KO #2           3027.78     747.87    63.00
## poly(Olaparib, 3)3:genotypeALC1 KO #2          -1095.97     747.87    63.00
## poly(Olaparib, 3)1:genotypeALC1 KO #3          -2371.95     747.87    63.00
## poly(Olaparib, 3)2:genotypeALC1 KO #3           2323.84     747.87    63.00
## poly(Olaparib, 3)3:genotypeALC1 KO #3           -998.31     747.87    63.00
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #1   955.00     747.87    63.00
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #1   977.10     747.87    63.00
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #1  -220.56     747.87    63.00
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #2  2525.30     747.87    63.00
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #2   528.20     747.87    63.00
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #2  -252.01     747.87    63.00
##                                                t value Pr(>|t|)    
## (Intercept)                                      9.365 6.03e-09 ***
## poly(Olaparib, 3)1                             -11.860  < 2e-16 ***
## poly(Olaparib, 3)2                              -2.543 0.013456 *  
## poly(Olaparib, 3)3                               0.696 0.489069    
## genotypePARP1 KO                                -0.771 0.449579    
## genotypeALC1 KO #1                              -1.509 0.146202    
## genotypeALC1 KO #2                              -0.395 0.696616    
## genotypeALC1 KO #3                              -0.973 0.341701    
## genotypeALC1 KO PARP1 KO #1                      2.643 0.015224 *  
## genotypeALC1 KO PARP1 KO #2                     -1.383 0.181067    
## poly(Olaparib, 3)1:genotypePARP1 KO              4.327 5.51e-05 ***
## poly(Olaparib, 3)2:genotypePARP1 KO             -0.128 0.898715    
## poly(Olaparib, 3)3:genotypePARP1 KO             -1.586 0.117706    
## poly(Olaparib, 3)1:genotypeALC1 KO #1           -3.424 0.001089 ** 
## poly(Olaparib, 3)2:genotypeALC1 KO #1            3.757 0.000378 ***
## poly(Olaparib, 3)3:genotypeALC1 KO #1           -1.702 0.093708 .  
## poly(Olaparib, 3)1:genotypeALC1 KO #2           -5.137 2.92e-06 ***
## poly(Olaparib, 3)2:genotypeALC1 KO #2            4.049 0.000143 ***
## poly(Olaparib, 3)3:genotypeALC1 KO #2           -1.465 0.147767    
## poly(Olaparib, 3)1:genotypeALC1 KO #3           -3.172 0.002343 ** 
## poly(Olaparib, 3)2:genotypeALC1 KO #3            3.107 0.002831 ** 
## poly(Olaparib, 3)3:genotypeALC1 KO #3           -1.335 0.186720    
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #1   1.277 0.206302    
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #1   1.307 0.196128    
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #1  -0.295 0.769026    
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO #2   3.377 0.001261 ** 
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO #2   0.706 0.482620    
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO #2  -0.337 0.737255    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

cat("AIC: ", AIC(fit12))

## AIC:  1232.853

simres <- simulateResiduals(fittedModel = fit12)
plot(simres)

Compare Results

ICtab(fit1,fit2,fit3,fit4,
      fit5,fit6,fit7,fit8,
      fit9,fit10,fit11,fit12,
      base=T)

##       AIC    dAIC   df
## fit11 -285.2    0.0 29
## fit7  -284.2    1.1 22
## fit3  -244.4   40.8 15
## fit10 -187.5   97.7 29
## fit6  -186.8   98.4 22
## fit2  -150.2  135.0 15
## fit12 1232.9 1518.1 30
## fit8  1328.7 1613.9 23
## fit4  1484.3 1769.5 16
## fit1  1724.8 2010.0 18
## fit5  1731.2 2016.4 25
## fit9  1743.3 2028.5 32

Final Result

fit <- fit7

output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]

rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )

# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in WT	-2.1289359	0.1641156	-12.9721723	0.0000000
Olaparib2 in WT	-0.4597677	0.1641156	-2.8014868	0.0062139
Olaparib1: WT vs. PARP1 KO	0.8370732	0.2320945	3.6066046	0.0005064
Olaparib2: WT vs. PARP1 KO	-0.1750941	0.2320945	-0.7544085	0.4525518
Olaparib1: WT vs. ALC1 KO #1	-0.7505743	0.2320945	-3.2339164	0.0017021
Olaparib2: WT vs. ALC1 KO #1	1.0165248	0.2320945	4.3797878	0.0000317
Olaparib1: WT vs. ALC1 KO #2	-0.6688214	0.2320945	-2.8816770	0.0049338
Olaparib2: WT vs. ALC1 KO #2	0.9436976	0.2320945	4.0660054	0.0001015
Olaparib1: WT vs. ALC1 KO #3	-0.6018346	0.2320945	-2.5930584	0.0110840
Olaparib2: WT vs. ALC1 KO #3	0.8060030	0.2320945	3.4727359	0.0007904
Olaparib1: WT vs. ALC1 KO PARP1 KO #1	0.7148231	0.2320945	3.0798791	0.0027383
Olaparib2: WT vs. ALC1 KO PARP1 KO #1	0.3506814	0.2320945	1.5109423	0.1342680
Olaparib1: WT vs. ALC1 KO PARP1 KO #2	0.4476996	0.2320945	1.9289539	0.0568530
Olaparib2: WT vs. ALC1 KO PARP1 KO #2	-0.0071566	0.2320945	-0.0308348	0.9754688

write.table(output, file = "Figure1D_Stats_Ref_WT.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

# re-fit with ALC1KO reference
dataset$genotype <- relevel(dataset$genotype, ref = "ALC1 KO #1")
        
fit <- lm(NormCounts2 ~ poly(Olaparib, 2)*genotype, data = dataset)


output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]

rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )

# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in ALC1 KO #1	-2.8795102	0.1641156	-17.5456207	0.0000000
Olaparib2 in ALC1 KO #1	0.5567570	0.1641156	3.3924686	0.0010269
Olaparib1: ALC1 KO #1 vs. WT	0.7505743	0.2320945	3.2339164	0.0017021
Olaparib2: ALC1 KO #1 vs. WT	-1.0165248	0.2320945	-4.3797878	0.0000317
Olaparib1: ALC1 KO #1 vs. PARP1 KO	1.5876474	0.2320945	6.8405210	0.0000000
Olaparib2: ALC1 KO #1 vs. PARP1 KO	-1.1916188	0.2320945	-5.1341964	0.0000016
Olaparib1: ALC1 KO #1 vs. ALC1 KO #2	0.0817528	0.2320945	0.3522394	0.7254732
Olaparib2: ALC1 KO #1 vs. ALC1 KO #2	-0.0728272	0.2320945	-0.3137825	0.7544044
Olaparib1: ALC1 KO #1 vs. ALC1 KO #3	0.1487396	0.2320945	0.6408580	0.5232256
Olaparib2: ALC1 KO #1 vs. ALC1 KO #3	-0.2105218	0.2320945	-0.9070519	0.3667750
Olaparib1: ALC1 KO #1 vs. ALC1 KO PARP1 KO #1	1.4653973	0.2320945	6.3137955	0.0000000
Olaparib2: ALC1 KO #1 vs. ALC1 KO PARP1 KO #1	-0.6658433	0.2320945	-2.8688455	0.0051209
Olaparib1: ALC1 KO #1 vs. ALC1 KO PARP1 KO #2	1.1982739	0.2320945	5.1628703	0.0000014
Olaparib2: ALC1 KO #1 vs. ALC1 KO PARP1 KO #2	-1.0236813	0.2320945	-4.4106226	0.0000282

write.table(output, file = "Figure1D_Stats_Ref_ALC1.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

Anova

fit7a <- lm(NormCounts2 ~ poly(Olaparib, 2)*genotype, data = dataset)
fit7b <- lm(NormCounts2 ~ poly(Olaparib, 2)+genotype, data = dataset)

# anova table
anova(fit7a, fit7b)

## Analysis of Variance Table
## 
## Model 1: NormCounts2 ~ poly(Olaparib, 2) * genotype
## Model 2: NormCounts2 ~ poly(Olaparib, 2) + genotype
##   Res.Df     RSS  Df Sum of Sq      F    Pr(>F)    
## 1     91 0.35014                                   
## 2    103 0.96183 -12  -0.61169 13.248 2.661e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1